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Improved Integrated Deep Model for Pap-Smear Cell Analysis

Journal of Systems Science and Information 2024年第1期12卷 113-124页

作者： somasundaram devaraj Nirmala MADIAN Gnanasaravanan SUBRAMANIAM Rithaniya CHELLAMUTHU Muralitharan KRISHANAN School of Electronics Engineering Vellore Institute of TechnologyVellore 632014India Department of Biomedical Engineering Dr.N.G.P Institute of TechnologyCoimbatore 641045India Karunya Institute of Technology and Sciences Coimbatore 641114India Department of Computer Science Institute of Mathematical ScienceSungkyunkwan UniversitySuwon 16419South Korea

Cervical cancer is the fourth most common malignancy to strike a woman *** discovered early enough,it can be effectively *** there is a chance of error owing to human error,the Pap smear is a good tool for first screening for cervical *** also takes a lot of time and effort to *** aim of this study was to reduce the possibility of error by automating the process of classifying cervical cancer using Pap smear *** the purpose of this study,dual convolution neural networks with LSTM were employed to classify images due to deep learning approaches inspire distinct features and powerful classifiers for many computer vision *** proposed deep learning model based on convolution neural networks(CNN)with the long short-term memory(LSTM)network is to learn features which give better recognition *** overall model is known as *** which‘smear’indicates‘pap-smear cancer cells’and‘net’refers to neural *** parameters such as,Accuracy,Precision,Recall,Accuracy,Sensitivity,and Specificity are used to validate the *** proposed method provides the improved accuracy of 99.57 percentage for classification of the pap-smear *** proposed approaches demonstrate the effectiveness of our contributions by testing and comparing with the state-of-the-art techniques.

关键词： pap-smear deep learning convolution neural network smear-net

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Early Detection Glaucoma and Stargardt’s Disease Using Deep Learning Techniques

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Intelligent Automation & Soft Computing 2023年第5期36卷 1283-1299页

作者： somasundaram devaraj Senthil Kumar Arunachalam Department of Electronics and Communication Engineering Sri Shakthi Institute of Engineering and TechnologyCoimabatore641062TamilnaduIndia

Retinal fundus images are used to discover many *** Machine learning algorithms are designed to identify the Glaucoma *** the accuracy and time consumption performance were not *** address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regres-sive Segmentation based Radial Basis Image Classiﬁer(MPCNKFTRS-RBIC)Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal *** MPCNKFTRS-RBIC Model,the ret-inal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuanﬁ***,preprocessed retinal fundus is given for hidden layer 2 for extracting the features like color,intensity,texture with higher *** extracting these features,the Tobit Regressive Segmenta-tion process is performed by hidden layer 3 for partitioning preprocessed image within more segments by analyzing the pixel with the extracted features of the fundus ***,the segmented image was given to output *** radial basis function analyzes the testing image region of a particular class as well as training image region with higher accuracy and minimum time *** is performed with retinal fundus image dataset with various perfor-mance metrics namely peak signal-to-noise ratio,accuracy and time,error rate concerning several retina fundus image and image size.

关键词： Glaucoma detection max pool convolution neural network kuanﬁlter radial basis function

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